Building epidemic models for living populations and computer networks

Area of Science:

• Epidemiology
• Mathematical Modeling
• Public Health

Background:

• Accurate viral outbreak modeling is crucial for resource management and effective public health interventions.
• The COVID-19 pandemic highlighted deficiencies in health resource planning in many countries.
• Simple yet realistic epidemic models are sought after by researchers.

Purpose of the Study:

• To present a unique epidemic model integrating stochastic processes and queuing theory.
• To evaluate the model's accuracy using computer simulations and real-world COVID-19 data.
• To provide a tool for predicting epidemic trends and informing resource planning.

Main Methods:

• Developed a novel epidemic model combining stochastic processes and queuing theory.
• Utilized computer simulations for model evaluation.
• Incorporated pre-processed COVID-19 data from an urban clinic and a local corona-center for parameterization and validation.

Main Results:

• The model accurately simulated COVID-19 transmission and recovery dynamics over a one-year period.
• Simulated results showed good agreement with current case data from the sample corona center.
• The model demonstrated the importance of accurate data for predicting future threats and mitigation costs.

Conclusions:

• The presented stochastic modeling approach, integrated with queuing theory and simulation, offers a concise and effective method for analyzing epidemic dynamics.
• This methodology can guide accurate epidemic modeling and classification of disease stages.
• Effective epidemic management relies on predictive models for resource allocation and risk mitigation.